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RID-Noise: Towards Robust Inverse Design under Noisy Environments [article]

Jia-Qi Yang, Ke-Bin Fan, Hao Ma, De-Chuan Zhan
2021 arXiv   pre-print
To achieve a data-efficient robust design, we propose Robust Inverse Design under Noise (RID-Noise), which can utilize existing noisy data to train a conditional invertible neural network (cINN).  ...  With the visual results from experiments, we clearly justify how RID-Noise works by learning the distribution and robustness from data.  ...  In this work, we propose a Robust Inverse Design under Noise (RID-Noise) method to tackle inverse design problems which demand robustness.  ... 
arXiv:2112.03912v1 fatcat:up6zw42tybgjfdtgvssykp6o7m